Filters








14,422 Hits in 5.7 sec

Dynamic Graph Neural Networks for Sequential Recommendation [article]

Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, Liang Wang
2021 arXiv   pre-print
We propose a new method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects different user sequences through a dynamic graph structure, exploring the interactive behavior  ...  Furthermore, we design a Dynamic Graph Recommendation Network to extract user's preferences from the dynamic graph.  ...  To this end, inspired by dynamic graph representation learning [18] , we propose a novel method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which explores interactive behaviors  ... 
arXiv:2104.07368v2 fatcat:74kljncl4nfefeehuqjg6g47du

Multi-Behavior Sequential Recommendation with Temporal Graph Transformer

Lianghao Xia, Chao Huang, Yong Xu, Jian Pei
2022 IEEE Transactions on Knowledge and Data Engineering  
Hence, sequential recommender systems have been developed to learn the dynamic user interests from the historical interactions for suggesting items.  ...  Towards this end, we propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns, by exploring the evolving  ...  ACKNOWLEDGMENTS We thank the reviewers for their valuable feedback and comments. This  ... 
doi:10.1109/tkde.2022.3175094 fatcat:iqreqptfvbeeffmit4isv7xsuu

Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation [article]

Chao Huang, Jiahui Chen, Lianghao Xia, Yong Xu, Peng Dai, Yanqing Chen, Liefeng Bo, Jiashu Zhao, Jimmy Xiangji Huang
2021 arXiv   pre-print
However, the majority of existing session-based recommendation techniques (e.g., attention-based recurrent network or graph neural network) are not well-designed for capturing the complex transition dynamics  ...  with the global graph context.  ...  Acknowledgments We thank the anonymous reviewers for their constructive feedback and comments.  ... 
arXiv:2110.03996v1 fatcat:qp5o3osmofgttnnas7r6b6lowu

Hyperbolic Hypergraphs for Sequential Recommendation [article]

Yicong Li, Hongxu Chen, Xiangguo Sun, Zhenchao Sun, Lin Li, Lizhen Cui, Philip S. Yu, Guandong Xu
2021 arXiv   pre-print
To alleviate the negative impact of sparse hypergraphs, we utilize a hyperbolic space-based hypergraph convolutional neural network to learn the dynamic item embeddings.  ...  To tackle the above shortcomings of the existing hypergraph-based sequential recommendations, we propose a novel architecture named Hyperbolic Hypergraph representation learning method for Sequential Recommendation  ...  RELATED WORK 5.1 Non-graph sequential modelling for neural recommendation Early neural recommendations building on typical deep neural networks mostly use recurrent neural networks (RNN) or convolutional  ... 
arXiv:2108.08134v1 fatcat:zifmaxbgovdffazhmeyop447y4

TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations [article]

Zijian Li, Ruichu Cai, Fengzhu Wu, Sili Zhang, Hao Gu, Yuexing Hao, Yuguang
2022 arXiv   pre-print
In this paper, we incorporate dynamic user-item heterogeneous graphs to propose a novel sequential recommendation framework.  ...  Sequential recommendation aims to choose the most suitable items for a user at a specific timestamp given historical behaviors.  ...  Beginning from the original conditional random field, we derive the unified objective function for the sequential recommendation, which leverages the social influence between users and the dynamic user-item  ... 
arXiv:2111.07378v2 fatcat:mkijhqypnvdbnjdjbzvc66ipyu

Inter-sequence Enhanced Framework for Personalized Sequential Recommendation [article]

Feng Liu, Weiwen Liu, Xutao Li, Yunming Ye
2020 arXiv   pre-print
Firstly, we equip graph neural networks in the inter-sequence correlation encoder to capture the high-order item correlation from the user-item bipartite graph and the item-item graph.  ...  To make better use of such information, we propose an inter-sequence enhanced framework for the Sequential Recommendation (ISSR).  ...  In addition to the RNN-based methods, Convolutional Neural Network (CNN) is also adopted for sequential recommendation (Tang and Wang 2018; Yuan et al. 2019; .  ... 
arXiv:2004.12118v2 fatcat:aunryocosjhgrfbdjn4a7eoq7m

An Implicit Preference-Aware Sequential Recommendation Method Based on Knowledge Graph

Haiyan Wang, Kaiming Yao, Jian Luo, Yi Lin, Honghao Gao
2021 Wireless Communications and Mobile Computing  
In order to address these above two problems, we propose an implicit preference-aware sequential recommendation method based on knowledge graph (IPAKG).  ...  Secondly, we integrate recurrent neural network and attention mechanism to capture user's evolving interests and relationships between different items in the sequence.  ...  [4] used a recurrent neural network for sequential recommendation and achieved a better performance boost. Quadrana et al.  ... 
doi:10.1155/2021/5206228 fatcat:dmn2323kyfh2nppavmyhse4dfe

Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations

Liwei Huang, Yutao Ma, Yanbo Liu, Bohong Danny Du, Shuliang Wang, Deyi Li
2022 ACM Transactions on Information Systems  
G raph C onvolutional N etwork (PTGCN) for the sequential recommendation.  ...  Most of the existing deep learning-based approaches utilize the recurrent neural network architecture or self-attention to model the sequential patterns and temporal influence among a user's historical  ...  Compared with the Markov chain-based approach, the recommendation approach based on neural networks, such as recurrent neural networks (RNNs) [6] , [7] , convolutional neural networks (CNNs) [8] , and  ... 
doi:10.1145/3511700 fatcat:5jbvfmzbqng7lcegj5eeqk33uu

When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation [article]

Yu Tian and Jianxin Chang and Yannan Niu and Yang Song and Chenliang Li
2022 arXiv   pre-print
Sequential recommendation aims at identifying the next item that is preferred by a user based on their behavioral history.  ...  Afterwards, a novel sequential capsule network is proposed to inject the sequential patterns into the multi-interest extraction process, leading to a more precise interest learning in a multi-grained manner  ...  To address this problem, some efforts propose to combine the sequential modeling with graph neural networks [2, 6] .  ... 
arXiv:2205.01286v1 fatcat:wlct2okdyrfbtjvllpb6f5fhmi

Star Graph Neural Networks for Session-based Recommendation

Zhiqiang Pan, Fei Cai, Wanyu Chen, Honghui Chen, Maarten de Rijke
2020 Proceedings of the 29th ACM International Conference on Information & Knowledge Management  
We propose Star Graph Neural Networks with Highway Networks (SGNN-HN) for session-based recommendation.  ...  Thus graph neural network (GNN) based models have been proposed to capture the transition relationship between items.  ...  in graph neural networks for session-based recommendation  ... 
doi:10.1145/3340531.3412014 dblp:conf/cikm/PanCCCR20 fatcat:gi5jjtxocjhl7l5rzmyibpro34

HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation [article]

Vijaikumar M, Deepesh Hada, Shirish Shevade
2021 arXiv   pre-print
In this work, we propose HyperTeNet -- a self-attention hypergraph and Transformer-based neural network architecture for the personalized list continuation task to address the challenges mentioned above  ...  We use graph convolutions to learn the multi-hop relationship among the entities of the same type and leverage a self-attention-based hypergraph neural network to learn the ternary relationships among  ...  Multi-view Graph Neural Network (MGNN) First, we randomly initialize the embeddings for users, items, and lists.  ... 
arXiv:2110.01467v2 fatcat:todwosunifbjlb5z5lm2tkttia

Sequential Recommendation with Graph Neural Networks [article]

Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, Yong Li
2021 arXiv   pre-print
In this work, we propose a graph neural network model called SURGE (short for SeqUential Recommendation with Graph neural nEtworks) to address these two issues.  ...  Sequential recommendation aims to leverage users' historical behaviors to predict their next interaction. Existing works have not yet addressed two main challenges in sequential recommendation.  ...  There are some works [33] utilizing graph neural networks for session-based recommendation, a problem similar with sequential recommendation.  ... 
arXiv:2106.14226v1 fatcat:c6inmah6qrh2vlsybo6viftwci

Enhancing Sequential Recommendation with Graph Contrastive Learning [article]

Yixin Zhang, Yong Liu, Yonghui Xu, Hao Xiong, Chenyi Lei, Wei He, Lizhen Cui, Chunyan Miao
2022 arXiv   pre-print
This paper proposes a novel recommendation framework, namely Graph Contrastive Learning for Sequential Recommendation (GCL4SR).  ...  The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors.  ...  Simultaneously, Convolutional Neural Networks (CNN) have also been explored for sequential recommendation.  ... 
arXiv:2205.14837v2 fatcat:4vtwqrtvpbevhmrx5hjlgu5esu

Graph Contextualized Self-Attention Network for Session-based Recommendation

Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou
2019 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence  
In GC-SAN, we dynamically construct a graph structure for session sequences and capture rich local dependencies via graph neural network (GNN).  ...  In this paper, we propose a graph contextualized self-attention model (GC-SAN), which utilizes both graph neural network and self-attention mechanism, for session-based recommendation.  ...  Conclusion In this paper, we proposed a graph contextualized selfattention network (GC-SAN) based on graph neural network for session-based recommendation.  ... 
doi:10.24963/ijcai.2019/547 dblp:conf/ijcai/XuZLSXZFZ19 fatcat:ge2hv6gl4ffexokdrhhyzvccw4

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation [article]

Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang
2021 arXiv   pre-print
neural networks.  ...  ; and 3) temporal/sequential recommendation, which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions.  ...  for neural graph based representation learning.  ... 
arXiv:2104.13030v3 fatcat:7bzwaxcarrgbhe36teik2rhl6e
« Previous Showing results 1 — 15 out of 14,422 results